code: def _get_dataset():
      mnist = tf.keras.datasets.mnist
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train, x_test = x_train / 255.0, x_test / 255.0
      # Use subset of 60000 examples to keep unit test speed fast.
      x_train = x_train[0:1000]
      y_train = y_train[0:1000]
      return (x_train, y_train), (x_test, y_test)
  
file_path: model-optimization/tensorflow_model_optimization/python/core/common/keras/compression/algorithms/different_training_and_inference_test.py
imports: import os
  import tempfile
  import tensorflow
  from tensorflow_model_optimization.python.core.common.keras.compression.algorithms import different_training_and_inference
  from tensorflow_model_optimization.python.core.keras.testing import test_utils_mnist
label_desc: Use image node_id 2 for calling a global function with example usage: _get_dataset() and returns: x_train,  y_train,  x_test,  y_test